Using Waveform Analyses to Develop Pediatric Gait Indices

Use of gait summary measures with lower limb amputees

Gait summary measures have been developed as a convenient method to communicate overall gait pathology. These measures are primarily used in the context of paediatric cerebral palsy and their use remains largely untested in other disability groups. This study assessed the suitability of gait summary measures for use with lower limb amputees. Modified (m) versions of three published gait summary measures were investigated - the Gillette Gait Index (mGGI), the Gait Deviation Index (mGDI) and the Gait Profile Score (mGPS) in conjunction with the Movement Analysis Profile (MAP). Twenty unilateral lower limb amputees underwent three-dimensional gait analysis. All measures reported significant differences between levels of amputation on the prosthetic limb. The mGGI and mGPS detected significant differences between the levels of amputation on the intact side, but the mGDI did not. All gait summary measures were moderately to strongly correlated with leg-length normalised self-selected walking speed and strong correlations were reported between all measures. The MAP exposed common strategies in amputee gait and showed that sagittal hip and knee kinematics contributed predominantly to overall gait deviation in this population group. The mGGI, mGDI and mGPS identified, quantified and stratified gait pathology, indicating that any of the gait measures investigated in this study can be applied as outcome measures in research and case management in lower limb amputees.

Classification of rhythmic locomotor patterns in electromyographic signals using fuzzy sets

Background

Locomotor control is accomplished by a complex integration of neural mechanisms including a central pattern generator, spinal reflexes and supraspinal control centres. Patterns of muscle activation during walking exhibit an underlying structure in which groups of muscles seem to activate in united bursts. Presented here is a statistical approach for analyzing Surface Electromyography (SEMG) data with the goal of classifying rhythmic "burst" patterns that are consistent with a central pattern generator model of locomotor control.

Methods

A fuzzy model of rhythmic locomotor patterns was optimized and evaluated using SEMG data from a convenience sample of four able-bodied individuals. As well, two subjects with pathological gait participated: one with Parkinson's Disease, and one with incomplete spinal cord injury. Subjects walked overground and on a treadmill while SEMG was recorded from major muscles of the lower extremities. The model was fit to half of the recorded data using non-linear optimization and validated against the other half of the data. The coefficient of determination, R², was used to interpret the model's goodness of fit.

Results

Using four fuzzy burst patterns, the model was able to explain approximately 70-83% of the variance in muscle activation during treadmill gait and 74% during overground gait. When five burst functions were used, one function was found to be redundant. The model explained 81-83% of the variance in the Parkinsonian gait, and only 46-59% of the variance in spinal cord injured gait.

Conclusions

The analytical approach proposed in this article is a novel way to interpret multichannel SEMG signals by reducing the data into basic rhythmic patterns. This can help us better understand the role of rhythmic patterns in locomotor control.

Functional principal component analysis of H-reflex recruitment curves

The primary purpose of this study was to use functional principal component analysis (FPCA) to analyze Hoffman-reflex (H-reflex) recruitment curves. Smoothed and interpolated recruitment curves from 38 participants were used for analysis. Standard methods were used to calculate three discrete variables (i.e., H(max)/M(max) ratio, H(th), H(slp)). FPCA was then used to extract principal component functions (PCFs) from the processed recruitment curves. PCF scores were calculated to determine how much each PCF contributed to an individuals' recruitment curve. The analysis extracted three PCFs, and three sets of PCF scores. Correlation analyses and systematic variation in the PCF scores indicated that the scores for the first PCF were primarily correlated to H-reflex threshold (H(th)) and that the scores for the second and third PCFs were correlated to H-reflex magnitude (H(max)/M(max) ratio) and slope (H(slp)), respectively. In addition, results from the FPCA indicated that the first PCF explained 56.0% of the variance between all H-reflex recruitment curves, whereas the second and third PCFs explained 24.1% and 13.0%, respectively. The high correlations indicate FPCA-derived PCFs capture similar physiological information as the standard discrete variables and suggest that application of FPCA to H-reflex recruitment curves could be used in future studies to complement traditional analyses that investigate excitability of the motoneuron pool.